Data Science

While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.

Agility and reactivity. These are two words more likely now than ever to feature in a corporate strategy session. Today’s business landscape is, after all, highly dynamic: increasing competition, margin pressures and the threat of disruptive innovation all conspire to erode the market shares of the complacent. The public sector

The advent of a sharing economy has brought a sea of change to the way we commute in the city. The Lyfts of the world have made taxi riding convenient, affordable and safe. These rides have emerged as a strong alternative to public transport, clocking millions of rides per month

The Golden State Warriors are on top of the NBA after winning their second championship in three years. They have been in the finals for three consecutive years, set the regular season wins record at 72 wins in the 2015-16 season, and came one game away from going undefeated through

In recent years’ evidence has been mounting that points to a crisis in the reproducible results of scientific research. Reviews of papers in the fields of psychology and cancer biology found that only 40% and 10%, respectively, of the results, could be reproduced. Nature published the results of a survey of

As happens when boundless potential meets hard reality, enterprises now face a long, painful slog through the trenches of disillusionment and disappointment as they pursue the business transformation promised by Machine Learning for the Enterprise. The machine learning hype cycle is in overdrive, inflating expectations for magically easy and automated solutions to complex business problems decades

A long time ago, in January 2006, Business Week published an article entitled ‘Math Will Rock Your World” declaring, “There has never been a better time to be a mathematician.” The fact is that although this article is almost 15 years old, the article reinforces a consistently valid case for

This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Miroslav Batchkarov and other experts will be giving talks

Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a

Whilst most businesses don’t earn revenue by processing data, they do spend a large amount of their hard earned revenue in manually processing data, validating it and ultimately performing manual tasks that don’t scale. But at what point does this manual involvement become a burden of cost upon your business?

Big data sets are so complex and large that common data processing tools and technologies cannot cope with them. The process of inspection of such data and uncovering patterns is called big data analytics. The basic question which arises in our mind is, “In what way is the drug discovery